docstr: Normalize the array to have Lp norm.
  
  Parameters
  ----------
  self
      Input array.
  p
      p-norm to use for normalization.
  axis
      Axis along which to normalize. If ``None``, the whole array
      is normalized.
  out
      optional output array, for writing the result to. It must have a
      shape that the inputs broadcast to.
  
  Returns
  -------
  ret
      The normalized array.
  
  Examples
  --------
  >>> x = ivy.array([[1., 2.], [3., 4.]])
  >>> y = x.lp_normalize(p=2, axis=1)
  >>> print(y)
  ivy.array([[0.44721359, 0.89442718],
         [0.60000002, 0.80000001]])
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code: def lp_normalize(
      self: ivy.Array,
      /,
      *,
      p: float = 2,
      axis: Optional[int] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Normalize the array to have Lp norm.
  
      Parameters
      ----------
      self
          Input array.
      p
          p-norm to use for normalization.
      axis
          Axis along which to normalize. If ``None``, the whole array
          is normalized.
      out
          optional output array, for writing the result to. It must have a
          shape that the inputs broadcast to.
  
      Returns
      -------
      ret
          The normalized array.
  
      Examples
      --------
      >>> x = ivy.array([[1., 2.], [3., 4.]])
      >>> y = x.lp_normalize(p=2, axis=1)
      >>> print(y)
      ivy.array([[0.44721359, 0.89442718],
             [0.60000002, 0.80000001]])
      """
      return ivy.lp_normalize(self, p=p, axis=axis, out=out)
  
file_path: ivy/ivy/data_classes/array/experimental/norms.py
imports: import abc
  from typing import Optional, Union, Tuple
  import ivy
label_desc: Use image node_id 6 for calling the _ArrayWithNormsExperimental obj's underlying member method code with example usage: obj.lp_normalize() and returns: ivy